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Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people

BACKGROUND: Sleep disturbance has become a considerable factor affecting the quality of life for middle-aged and elderly people; however, there are still many obstacles to screening sleep disturbance for those people. Given the growing awareness of the association between gastrointestinal function a...

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Autores principales: Ji, Shuming, Li, Baichuan, Zhu, Chenxing, Jiang, Guohui, Tang, Yusha, Chen, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327604/
https://www.ncbi.nlm.nih.gov/pubmed/37426096
http://dx.doi.org/10.3389/fpsyt.2023.1183108
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author Ji, Shuming
Li, Baichuan
Zhu, Chenxing
Jiang, Guohui
Tang, Yusha
Chen, Lei
author_facet Ji, Shuming
Li, Baichuan
Zhu, Chenxing
Jiang, Guohui
Tang, Yusha
Chen, Lei
author_sort Ji, Shuming
collection PubMed
description BACKGROUND: Sleep disturbance has become a considerable factor affecting the quality of life for middle-aged and elderly people; however, there are still many obstacles to screening sleep disturbance for those people. Given the growing awareness of the association between gastrointestinal function and sleep disturbance, our study aims to predict the risk of sleep disturbance using gastrointestinal electrophysiological signals. METHODS: The Pittsburgh Sleep Quality Index and gastrointestinal electrophysiological signals of 914 participants in western China were used to establish the model. Demographic characteristics and routine blood test were collected as covariates. Participants were randomly assigned into two sets with a 7:3 ratio for training and validation. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were used, respectively for variables selection and optimization. To assess the model performance, receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were utilized. Then, validation was performed. RESULTS: Thirteen predictors were chosen from 46 variables by LASSO regression. Then, age, gender, percentage of normal slow wave and electrical spreading rate on the pre-meal gastric channel, dominant power ratio on the post-meal gastric channel, coupling percent and dominant frequency on the post-meal intestinal channel were the seven predictors reserved by logistic regression. The area under ROC curve was 0.65 in the training set and 0.63 in the validation set, both exhibited moderate predictive ability. Furthermore, by overlapping the DCA results of two data-sets, there might be clinical net benefit if 0.35 was used as reference threshold for high risk of sleep disturbance. CONCLUSION: The model performs a worthy predictive potency for sleep disturbance, which not only provides clinical evidence for the association of gastrointestinal function with sleep disturbance, but also can be considered as an auxiliary assessment for screening sleep disturbance.
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spelling pubmed-103276042023-07-08 Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people Ji, Shuming Li, Baichuan Zhu, Chenxing Jiang, Guohui Tang, Yusha Chen, Lei Front Psychiatry Psychiatry BACKGROUND: Sleep disturbance has become a considerable factor affecting the quality of life for middle-aged and elderly people; however, there are still many obstacles to screening sleep disturbance for those people. Given the growing awareness of the association between gastrointestinal function and sleep disturbance, our study aims to predict the risk of sleep disturbance using gastrointestinal electrophysiological signals. METHODS: The Pittsburgh Sleep Quality Index and gastrointestinal electrophysiological signals of 914 participants in western China were used to establish the model. Demographic characteristics and routine blood test were collected as covariates. Participants were randomly assigned into two sets with a 7:3 ratio for training and validation. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were used, respectively for variables selection and optimization. To assess the model performance, receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were utilized. Then, validation was performed. RESULTS: Thirteen predictors were chosen from 46 variables by LASSO regression. Then, age, gender, percentage of normal slow wave and electrical spreading rate on the pre-meal gastric channel, dominant power ratio on the post-meal gastric channel, coupling percent and dominant frequency on the post-meal intestinal channel were the seven predictors reserved by logistic regression. The area under ROC curve was 0.65 in the training set and 0.63 in the validation set, both exhibited moderate predictive ability. Furthermore, by overlapping the DCA results of two data-sets, there might be clinical net benefit if 0.35 was used as reference threshold for high risk of sleep disturbance. CONCLUSION: The model performs a worthy predictive potency for sleep disturbance, which not only provides clinical evidence for the association of gastrointestinal function with sleep disturbance, but also can be considered as an auxiliary assessment for screening sleep disturbance. Frontiers Media S.A. 2023-06-23 /pmc/articles/PMC10327604/ /pubmed/37426096 http://dx.doi.org/10.3389/fpsyt.2023.1183108 Text en Copyright © 2023 Ji, Li, Zhu, Jiang, Tang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Ji, Shuming
Li, Baichuan
Zhu, Chenxing
Jiang, Guohui
Tang, Yusha
Chen, Lei
Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title_full Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title_fullStr Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title_full_unstemmed Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title_short Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
title_sort risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327604/
https://www.ncbi.nlm.nih.gov/pubmed/37426096
http://dx.doi.org/10.3389/fpsyt.2023.1183108
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